Objective:
We will provide reclamation managers, Office of Surface Mining Reclamation and Enforcement (OSM) and coal mining states with practical knowledge that helps them design consistently effective reclamation plans. Our specific objectives are to:
1) Assemble existing data on managerial (e.g. seed mixes, tillage practices) and environmental (e.g. soil type, precipitation during fall of seeding) predictors that regulate annual brome invasion from 80 sites distributed across eight mines and three states.
2) Measure cover of each plant species during two consecutive years at the 80 sites.
3) Measure soil properties and landscape attributes at the 80 sites.
4) Use data from Objectives 1-3 to develop a statistical model that forecasts levels of annual brome invasion resulting from specified reclamation plans.
5) Make reclamation forecasts available to all relevant parties in an easy-to-use format by giving presentations at coal mines and annual society conferences, publishing at least one peer-reviewed journal article, developing an interactive, web-available forecasting model (if results dictate this is necessary), and employing all additional means necessary.

Approach:
Our team will work with vast stores of existing data from 80 sites distributed across eight mines and three states. We will acquire the existing data from reclamation plans and progress reports and personal communications with reclamation managers. The existing data will describe seed mixes, tillage, soils, climate, vegetation and other attributes of past reclamation. Our proposed field sampling will provide additional soils and vegetation data we will need for our project.
We will use the data to develop a model that forecasts reclamation outcomes. For example, we will devise forecasts of the following form: “There is a 95% chance that annual bromes will make up between 20% and 40% of total plant cover if a mixture of western wheatgrass (Pascopyrum smithii) and green needlegrass (Stipa viridula) is seeded at Doe Mine.” Of course, this interval is fictitious, and more than just the seeded species will need to be specified to make real predictions. Nevertheless, this example clearly illustrates the types of forecasts our project will provide.